Bøger af Utku Kose

This book highlights the use of explainable artificial intelligence (XAI) for healthcare problems, in order to improve trustworthiness, performance and sustainability levels in the context of applications.Explainable Artificial Intelligence (XAI) in Healthcare adopts the understanding that AI solutions should not only have high accuracy performance, but also be transparent, understandable and reliable from the end user's perspective. The book discusses the techniques, frameworks, and tools to effectively implement XAI methodologies in critical problems of healthcare field. The authors offer different types of solutions, evaluation methods and metrics for XAI and reveal how the concept of explainability finds a response in target problem coverage. The authors examine the use of XAI in disease diagnosis, medical imaging, health tourism, precision medicine and even drug discovery. They also point out the importance of user perspectives and value of the data used in target problems. Finally, the authors also ensure a well-defined future perspective for advancing XAI in terms of healthcare.This book will offer great benefits to students at the undergraduate and graduate levels and researchers. The book will also be useful for industry professionals and clinicians who perform critical decision-making tasks.

In today's world, smart healthcare supports the out-of-hospital concept, which transforms and offers higher care standards. This is accomplished with individual requirements with the help of public opinion. Moreover, smart healthcare systems are generally designed to sense individual health status data, which can be forwarded to clinical professionals for interpretation. Swarm intelligence analysis is a valuable tool for categorizing public opinion into different sentiments. Dynamics of Swarm Intelligence Health Analysis for the Next Generation discusses the role of behavioral activity in the evolution of traditional medical systems to intelligent systems. It further focuses on the economic, social, and environmental impacts of swarm intelligence smart healthcare systems. Covering topics such as healthcare data analytics, clustering algorithms, and the internet of medical things, this premier reference source is an excellent resource for healthcare professionals, hospital administrators, IT managers, policymakers, educators and students of higher education, researchers, and academicians.

This book presents research on how interpretable cognitive IoT can work to help with the massive amount of data in the healthcare industry. The authors give importance to IoT systems with intense machine learning features; this ensures the scope corresponds to use of cognitive IoT for understanding, reasoning, and learning from medical data. The authors discuss the interpretability of an intelligent system and its trustworthiness as a smart tool in the context of massive healthcare applications. As a whole, book combines three important topics: massive data, cognitive IoT, and interpretability. Topics include health data analytics for cognitive IoT, usability evaluation of cognitive IoT for healthcare, interpretable cognitive IoT for health robotics, and wearables in the context of IoT for healthcare. The book acts as a useful reference work for a wide audience including academicians, scientists, students, and professionals.

This book explores various applications of deep learning-oriented diagnosis leading to decision support, while also outlining the future face of medical decision support systems.

The objective of this book is to focus on Explainable Artificial Intelligence (XAI) in smart city development. This book provides a timely, global reference source about cutting edge research efforts to ensure the XAI factor in smart city-oriented developments.

Though educational methods such as distance and e-learning have addressed our modern, knowledge-based society's requirement for innovative approaches to performing educational activities, room for improvement still exists. This title examines the efforts made to bridge the gap between student and educator with computer applications.